A damage effectiveness evaluation approach of warhead fragment group on missile target based on intuitionistic fuzzy neural network

Abstract

To address the difficulty in evaluating the damage effect of missile target attacked by fragmented warheads, this paper proposes a new damage assessment method. In this paper, the damage to the missile target is regarded as the result of the continuous action of multiple layers of warhead fragments at multiple times. Based on this damage mechanism, sample data is formed using the characteristic parameters of warhead fragments, by introducing intuitionistic fuzzy neural network (IFNN), a new missile target damage effect evaluation model based on IFNN is established. Finally, training and testing are conducted on the data of actual missile target intersection damage tests, and the results are compared with other target damage evaluation methods. The results show that this evaluation method can effectively obtain the damage value of the missile target, and the evaluation model has good generalization ability. This provides ideas for developing a new method to evaluate the static or dynamic damage effectiveness of intelligent ammunition.